地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (6): 969-982.doi: 10.12082/dqxxkx.2019.180536

• 遥感科学与应用技术 • 上一篇    

基于多源遥感与地形信息的北极城镇用地信息提取

梁立1,2(), 李鑫杨3, 刘庆生1,*(), 刘高焕1, 黄翀1, 李贺1   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 内蒙古赤峰气象局,赤峰 024000
  • 收稿日期:2018-10-22 修回日期:2019-01-24 出版日期:2019-06-15 发布日期:2019-07-03
  • 通讯作者: 刘庆生 E-mail:liangl.16s@igsnrr.ac.cn;liuqs@lreis.ac.cn
  • 作者简介:

    作者简介:梁立(1994-),男,河南漯河人,硕士,研究方向为遥感和地理信息系统应用。E-mail: liangl.16s@igsnrr.ac.cn

  • 基金资助:
    国家重点研发计划课题(2016YFC1402701)、国家自然科学基金项目(41801354)

Extraction of Arctic Urban Land Use Information based on Multi-source Remote Sensing and Topograph

Li LIANG1,2(), Xinyang LI3, Qingsheng LIU1,*(), Gaohuan LIU1, Chong HUANG1, He LI1   

  1. 1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Weather bureau of Chifeng, Chifeng 024000, China
  • Received:2018-10-22 Revised:2019-01-24 Online:2019-06-15 Published:2019-07-03
  • Contact: Qingsheng LIU E-mail:liangl.16s@igsnrr.ac.cn;liuqs@lreis.ac.cn
  • Supported by:
    National Key Research and Development Program of China, No.2016YFC1402701;National Natural Science Foundation of China, No.41801354

摘要:

北极城镇空间信息对于研究北极,认识北极,利用北极都有重要意义。本文以北极圈内的城市特罗姆瑟为例,使用Landsat影像、DMSP/OLS夜间灯光、ASTER-GDEM2数据,通过特征提取得到属于光谱特征、纹理特征、夜间灯光特征、地形特征等18个特征,识别最优特征组合后基于AdaBoost算法提取1990、2004、2016年研究区的城镇用地,并将提取结果与最大似然法进行了对比,在此基础上进行了扩张分析。研究结果表明:引入地形与夜间灯光特征都可以在光谱纹理特征的基础上提高提取精度。基于光谱与纹理特征得到的总体精度与kappa值分别为86.20%和0.68;加入地形特征后精度分别提高2.70%(OA)和6.21%(kappa);加入夜间灯光特征后精度分别提高2.10%和0.50;加入地形与夜间灯光特征精度分别提高3.70%和8.55%,因此光谱、纹理、地形与夜间灯光的组合是最优特征组合。通过最优特征组合进行AdaBoost城镇提取,比最大似然法对城镇的两分类总体精度高10%左右、kappa值高20%左右。计算结果显示,研究区城镇扩张强度为5.5×10-4左右,属于缓慢扩张;扩张的平均动态度水平为0.018,是全球水平(0.0325)的一半左右;2004-2016年的动态度水平低于1990-2004年的动态度水平,说明研究区目前由高速发展期向平稳发展期过渡。

关键词: 机器学习, Landsat, DMSP/OLS夜间灯光, DEM, 城镇提取, 特罗姆瑟, 北极城镇

Abstract:

As climate warms up and ice melts, the Arctic is drawing much more attention. It is undeniable that Arctic urban spatial information is critical for studying, understanding, and exploring the Arctic. Due to the special geographical situation, Arctic urban extraction has unique difficulties such as urban fragmentation and confusion with bare mountains. To overcome the problems of extracting Arctic urban, multi-source data including Landsat, DMSP/OLS, and ASTER-GDEM2 were used. Spectral features, texture features, nighttime light features, and topographic features were obtained after feature extraction. Apart from that, the AdaBoost algorithm was used to extract the urban areas at 1990, 2004 and 2016. To clearly and more completely understand the function of each feature, we divided features into four different groups, and compared their differences. The result shows that, adding terrain features or nighttime lighting features can improve the extraction accuracy, and that the combination of spectrum, texture, terrain, and nighttime lighting is the optimal combination of features. The overall accuracy (OA) and kappa values based on spectral and texture features are 86.20% and 0.68, respectively. After adding terrain feature, the accuracy increased by 2.7% (OA) and 6.21% (kappa) respectively. When only adding nighttime lighting feature, OA increased by 2.1% and kappa 0.50. The best result was reached when we added terrain feature and nighttime lighting simultaneously. In this case, the overall accuracy and kappa increased by 3.7% and 8.55%, respectively. So, it is the optimal combination of features. After identifying the optimal feature combination, the maximum likelihood method was used to extract urban areas to prove the effectiveness of the AdaBoost algorithm. Experiment results show that, with the optimal feature combination, extraction based on AdaBoost has its OA and kappa value 10% and 20% higher respectively than those by the maximum likelihood method. Finally, the urban expansion was analyzed. The intensity of the urban expansion in the study area is around 4.4×10-3 from 1990 to 2004 and this number is 4.5×10-3 from 2004 to 2016, which can be interpreted as slow expansion. The average level of expansion is 0.018, 1/2 of the global average. The urban expansion level between 1990 and 2004 is higher than that between 2004 and 2016. The difference in the dynamics during 1990-2004 and during 2004-2016 indicates that the study area is currently transitioning from a high-speed development period to a stable development period. Given the warming of the Arctic and the growing of population, Arctic urban is expected to continue expanding slowly.

Key words: AdaBoost algorithm, landsat, DMSP/OLS, DEM, urban extraction, Troms?, Arctic town